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Cross-Domain Recommendation based on K-Means Clustering and Transformer
| Vol. 23 No.5 | pp.1-8|Year 2023 Month 10
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Design and Optimality Analysis of Cache Performance Model for Multimedia Streaming Environments
| Vol. 23 No.5 | pp.9-13|Year 2023 Month 10
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Cycle Detection of Discrete Logarithm using an Array
| Vol. 23 No.5 | pp.15-20|Year 2023 Month 10
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Research on Security System for Safe Communication in Maritime Environment
| Vol. 23 No.5 | pp.21-27|Year 2023 Month 10
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Who Uses OTT Services and How Much Time They Spend There? Focusing on Socio-Demographic and Personality Characteristics
| Vol. 23 No.5 | pp.29-34|Year 2023 Month 10
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Design of Communication Module for Virtual Serial Wireless LAN
| Vol. 23 No.5 | pp.35-40|Year 2023 Month 10
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Design of EMC countermeasures for radar signal processing board
| Vol. 23 No.5 | pp.41-46|Year 2023 Month 10
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Adjustment System for Outlier and Missing Value using Data Storage
| Vol. 23 No.5 | pp.47-53|Year 2023 Month 10
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A Study on Emotional Response toward Virtual Influencers and Advertising Effectiveness
| Vol. 23 No.5 | pp.55-61|Year 2023 Month 10
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Musculoskeletal Rehabilitation Exercise Platform for Elderly based on MR
| Vol. 23 No.5 | pp.63-70|Year 2023 Month 10
Vol. 23 No.5 | pp.1-8.
Cross-Domain Recommendation based on K-Means Clustering and Transformer
Abstract Cross-domain recommendation is a method that shares related user information data and item data in different domains. It is mainly used in online shopping malls with many users or multimedia service contents, such as YouTube or Netflix. Through K-means clustering, embeddings are created by performing clustering based on user data and ratings. After learning the result through a transformer network, user satisfaction is predicted. Then, items suitable for the user are recommended using a transformer-based recommendation model. Through this study, it was shown through experiments that recommendations can predict cold-start problems at a lesser time cost and increase user...
